Test driving three 1995 genetic algorithms: New test functions and geometric matching

1995 ◽  
Vol 1 (1) ◽  
pp. 77-104 ◽  
Author(s):  
D. Whitley ◽  
R. Beveridge ◽  
C. Graves ◽  
K. Mathias
Keyword(s):  
Genetic Algorithms ◽  
Test Functions ◽  
Geometric Matching

A FITNESS-BASED MULTI-PARENT CROSSOVER OPERATOR WITH PROBABILISTIC SELECTION

2012 ◽  
Vol 21 (01) ◽  
pp. 1250005
Author(s):  
SURAPONG AUWATANAMONGKOL
Keyword(s):  
Genetic Algorithms ◽  
Threshold Value ◽  
Fast Convergence ◽  
Premature Convergence ◽  
Test Functions ◽  
Crossover Operator ◽  
Solution Quality ◽  
Early Stages ◽  
Optimal Setting ◽  
Conflicting Goals

Several multi-parent crossover operators have been proposed to increase the performance of genetic algorithms. In these cases, the operators allow several parents to simultaneously take part in creating offspring. However, the operators need to find a balance between the two conflicting goals of exploitation and exploration. Strong exploitation allows fast convergence to succeed but can lead to premature convergence while strong exploration can lead to better solution quality but slower convergence. This paper proposes a new fitness based scanning multi-parent crossover operator for genetic algorithms. The new operator seeks out the optimal setting for the two goals in order to achieve the highest benefits from both. The operator uses a probabilistic selection with an incremental threshold value to allow strong exploration in the early stages of the algorithms and strong exploitation in their later stages. Experiments conducted on some test functions show that the operator can give better solution quality and more convergence consistency when compared with some other well-known multi-parent crossover operators.

Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions

2000 ◽  
Vol 8 (4) ◽  
pp. 373-391 ◽  
Author(s):  
John H. Holland
Keyword(s):  
Genetic Algorithms ◽  
Reverse Engineering ◽  
General Class ◽  
Building Blocks ◽  
Test Functions ◽  
Search Spaces ◽  
Human Understanding

Building blocks are a ubiquitous feature at all levels of human understanding, from perception through science and innovation. Genetic algorithms are designed to exploit this prevalence. A new, more robust class of genetic algorithms, cohort genetic algorithms (cGA's), provides substantial advantages in exploring search spaces for building blocks while exploiting building blocks already found. To test these capabilities, a new, general class of test functions, the hyperplane-defined functions (hdf's), has been designed. Hdf's offer the means of tracing the origin of each advance in performance; at the same time hdf's are resistant to reverse engineering, so that algorithms cannot be designed to take advantage of the characteristics of particular examples.

scholarly journals EXPERIMENTAL ANALYSIS OF MULTINATIONAL GENETIC ALGORITHM AND ITS MODIFICATIONS

2021 ◽  
pp. 71-83
Author(s):  
N. M. Gulayeva ◽  
S. A. Yaremko
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Experimental Analysis ◽  
Optimization Problems ◽  
Analysis Of Algorithms ◽  
Fitness Function ◽  
Performance Criteria ◽  
Test Problems ◽  
Test Functions ◽  
Multimodal Function

Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms. Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function. Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence. Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out. Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.

scholarly journals On Some Aspects of Genetic and Evolutionary Methods for Optimization Purposes

2015 ◽  
Vol 61 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Marcin Woźniak ◽  
Dawid Połap
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Evolutionary Optimization ◽  
Optimization Techniques ◽  
Test Functions ◽  
Research Results ◽  
Hybrid Genetic Algorithms ◽  
Evolutionary Methods ◽  
Evolutionary Optimization Techniques ◽  
Potential Efficiency

Abstract In this paper, the idea of applying some hybrid genetic algorithms with gradient local search and evolutionary optimization techniques is formulated. For two different test functions the proposed versions of the algorithms have been examined. Research results are presented and discussed to show potential efficiency in optimization purposes

Structural Optimization Using Multi Evolutionary System Co-Exist Genetic Algorithm

2010 ◽  
Vol 450 ◽  
pp. 556-559
Author(s):  
Dong Mei Cheng ◽  
Chang Hua Qiu ◽  
Cheng Yang Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Convergence Rate ◽  
Structural Optimization ◽  
Local Convergence ◽  
Evolutionary System ◽  
Test Functions ◽  
Genetic Operators ◽  
Fitness Value ◽  
High Convergence Rate

Traditional genetic algorithms put all the individuals in one population to cross and adopt the same set of evolutionary parameters and genetic operators to guide the evolution, which will easily lead to local convergence and poor searching efficiency. A multi evolutionary system co-exist genetic algorithm is developed to overcome the fluctuations of the whole evolution process through dividing individuals into several sub-populations according to the fitness value. Moreover, the improved algorithm prevents the early convergent and increases the diversity of individuals by supplying these sub-populations different evolutionary systems. The effectiveness and feasibility of the algorithm are verified by typical genetic algorithm test functions and an engineering case. The results show that the genetic algorithm has a good versatility, high convergence rate and solution precision.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms
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